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Measuring social capital in a population-scale social network

September 13 , 14:39 14:59 UTC+1

Lecture by Bart de Zoete, Frank Takes, Eelke Heemskerk, Eszter Bokànyi and Yuliia Kazmina at 6th European Conference on Social Networks (EUSN 2022).

In this work we consider the measurement of social capital using population-scale social network data in the Netherlands. Social capital can be seen as the value and resources found in social structures which enable collective action. Having been linked to many societal phenomena, social capital has become a cornerstone of social science. It is most often measured indirectly using data on its expected outcomes, such as civic participation. Another approach, which we will utilize in this work, is to use social networks, which can capture the network structural aspect of social capital. However, with traditional social network data, the network aspect can be problematic due to data quality and completeness issues.
In this work, we bring large-scale and high quality social network data and data on four key social capital outcomes together, in order to for the first time at the scale of an entire population assess the power of network measures of social capital such as average degree and attribute assortativity. We consider a population-scale social network with formal ties (e.g., family, work, school, and neighbor relations) of the entire Dutch population. This network has unique properties that make it highly interesting and well-suited for the measurement of social capital, and its validation. Indeed, the network contains various node attributes that can be used to group people by the geographical neighborhood in which they reside. This in turn makes it possible to use existing neighborhood data (i.e., proxies of community social capital) to validate our measure. Various statistics about Dutch neighborhoods are publicly available, some of which relate to common social capital outcomes. We represent four social capital outcomes using the percentage of people with good perceived health, that do volunteer work, that receive social assistance benefits from the government, and the number of reported violent crimes per one thousand people in the neighborhood. We use regression models to assess the precise relation between network measures and these social capital outcomes. The results show that all four conceptualizations are to some extent measurable through structural network measures. Our work presents the first major steps for the measurement of social capital using population-scale network data. The findings can be valuable to anyone measuring social capital in networks, paving the way for informed decision making aimed at increasing social capital of, for example, minority groups.